Milad Lankarany

My lab develops advanced methods in Computational Neuroscience, Engineering, and Neurotechnology to uncover information processing mechanisms of neural systems, in order to treat neurological disorders and to advance biologically inspired computational frameworks. The main objective of our lab is to uncover information processing mechanisms of neural systems. Our goal is to understand how information is represented, propagated and computed. Understanding neural information processing will result in the development of computational algorithms and engineering techniques for optimal interfacing with the brain. The main challenge toward understanding the mechanisms of neural information processing is the difficulty of inferring information (e.g., coding mechanism of somatosensory system) from incomplete and sparse observations (e.g., neural activity (spikes) of few cortical neurons). This challenge is known as lack of observability, which is a measure that indicates how well the internal states of a system can be inferred from the knowledge of its external outputs. To meet our objective, we create and develop approaches to overcome the lack of observability in neural systems and to enhance the controllability of neural systems as follows: 1) Create and develop computational models and techniques to infer the hidden states (dynamics) of the neural systems from sparse observations; 2) Develop novel neuro-technology to increase accessibility to more observations.